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A Naïve Bayes Approach to Cross-Lingual Word Sense Disambiguation and Lexical Substitution

  • David Pinto
  • Darnes Vilariño
  • Carlos Balderas
  • Mireya Tovar
  • Beatriz Beltrán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

Word Sense Disambiguation (WSD) is considered one of the most important problems in Natural Language Processing [1]. It is claimed that WSD is essential for those applications that require of language comprehension modules such as search engines, machine translation systems, automatic answer machines, second life agents, etc. Moreover, with the huge amounts of information in Internet and the fact that this information is continuosly growing in different languages, we are encourage to deal with cross-lingual scenarios where WSD systems are also needed. On the other hand, Lexical Substitution (LS) refers to the process of finding a substitute word for a source word in a given sentence. The LS task needs to be approached by firstly disambiguating the source word, therefore, these two tasks (WSD and LS) are somehow related. In this paper, we present a naïve approach to tackle the problem of cross-lingual WSD and cross-lingual lexical substitution. We use a bilingual statistical dictionary, which is calculated with Giza++ by using the EUROPARL parallel corpus, in order to calculate the probability of a source word to be translated to a target word (which is assumed to be the correct sense of the source word but in a different language). Two versions of the probabilistic model are tested: unweighted and weighted. The results were compared with those of an international competition, obtaining a good performance.

Keywords

Target Word Machine Translation Ambiguous Word Source Language Statistical Machine Translation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aguirre, E., Edmonds, P.: Word Sense Disambiguation, Text, Speech and Language Technology. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Chan, Y., Ng, H., Chiang, D.: Word sense disambiguation improves statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 33–40 (2007)Google Scholar
  3. 3.
    Carpuat, M., Wu, D.: Improving statistical machine translation using word sense disambiguation. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLPCoNLL), pp. 61–72 (2007)Google Scholar
  4. 4.
    McCarthy, D., Navigli, R.: English lexical substitution task. In: SemEval 2007 Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 48–53 (2007)Google Scholar
  5. 5.
    Mihalcea, R., Sinha, R., McCarthy, D.: Semeval-2010 task2: cross-lingual lexical substitution. In: Proceedings of the Fifth International Workshop on Semantic Evaluations (SemEval-2010). Association for Computational Linguistics (2010)Google Scholar
  6. 6.
    Lefever, E., Hoste, V.: Semeval-2010 task3:cross-lingual word sense disambiguation. In: Proceedings of the Fifth International Workshop on Semantic Evaluations (SemEval-2010). Association for Computational Linguistics (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David Pinto
    • 1
  • Darnes Vilariño
    • 1
  • Carlos Balderas
    • 1
  • Mireya Tovar
    • 1
  • Beatriz Beltrán
    • 1
  1. 1.Faculty of Computer ScienceB. Autonomous University of PueblaMexico

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